DOYLE J.

Microcontroller Units (MCUs) are widely used for industrial field applications, and are now ever more being used also for machine learning on the edge, because of their reliability, low cost, and energy efficiency. Due to the MCU resource limitations, the deployed ML models need to be optimized particularly in terms of memory footprint. In this...

This paper investigated the application of unsupervised learning on a mainstream microcontroller, like the STM32 F4. We focused on the simple K-means technique, which achieved good accuracy levels on the four test datasets. These results are similar to those obtained by training a k-nearest neighbor (K-NN) classifier with the actual labels,...

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and...

Binarization is a machine learning optimization for limited resource devices that has achieved significant results in edge applications. As microcontrollers are the mainstream platform for field applications in industry, this article investigates memory efficient deployment of binary neural networks (BNNs) to such devices. To this end, we...

The quest for efficient Tiny Machine Learning on Microcontroller Units is increasing rapidly due to the vast application spectrum made possible with the advancement of Tiny ML. One application area that could benefit from such advancement is Electronic Skin systems, that are employed in several domains such as: wearable devices, robotics,...

Binarization is an extreme quantization technique that is attracting research in the Internet of Things (IoT) field, as it radically reduces the memory footprint of deep neural networks without a correspondingly significant accuracy drop. To support the effective deployment of Binarized Neural Networks (BNNs), we propose CBin-NN, a library of...